scholarly journals Collage technique may provide new perspectives for Alzheimer patients by exploring messages from their inner world

2009 ◽  
Vol 3 (4) ◽  
pp. 299-302 ◽  
Author(s):  
Mitsue Meguro ◽  
Junichi Ishizaki ◽  
Kenichi Meguro
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Cognitive Assessments ◽  
Dementia Patients ◽  
Inner World

Abstract Although the collage art technique has been introduced as a psychotherapeutic method, it has not been fully applied in dementia. Objectives: To analyze characteristics of the collage articles produced by patients with Alzheimer's disease (AD). Methods: Twenty AD patients were asked to select and place several clippings as they wished. The MMSE was used for cognitive assessments. Results: Simplification and poor organization in their articles were found. The themes of one patient were found to change according to behavior. We discussed the images of the articles, especially spiritual images in the early stage and family images in the later stage. Conclusions: We concluded that the collage technique could provide new perspectives for dementia patients by exploring messages from their inner world.

scholarly journals Screening for early-stage Alzheimer’s disease using optimized feature sets and machine learning

2020 ◽  
Author(s):  
Michael J. Kleiman ◽  
Elan Barenholtz ◽  
James E. Galvin ◽  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Linear Models ◽  
Early Stage ◽  
High Rate ◽  
Cognitive Assessments ◽  
High Detection Rate ◽  
Starting Point ◽  
Assessment Time ◽  
Mild Impairment

ABSTRACTBackgroundDetecting early-stage Alzheimer’s disease in clinical practice is difficult due to a lack of efficient and easily administered cognitive assessments that are sensitive to very mild impairment, a likely contributor to the high rate of undetected dementia.ObjectiveHere, we aim to identify groups of cognitive assessment features optimized for detecting mild impairment that can be used in routine screening. We also compare the efficacy of classifying impairment using either a two-class (impaired vs non-impaired) or three-class approach.MethodsSupervised feature selection methods generated groups of cognitive measurements targeting impairment defined at CDR 0.5 and above. Random forest classifiers then generated predictions of impairment for each group using highly stochastic cross-validation, with group outputs examined using general linear models.ResultsThe strategy of combining impairment levels for two-class classification resulted in significantly higher sensitivities and NPVs, two metrics useful in clinical screening, compared to the three-class approach. Just four neuropsychological features (delayed WAIS Logical Memory, trail-making, patient and informant memory questions), able to be administered in approximately 15 active minutes (∼30 minutes with delay), enabled classification sensitivity of 94.53% (88.43% PPV) with the addition of four more features significantly increasing sensitivity to 95.18% (88.77% PPV) when added to the model as a second classifier.ConclusionThe high detection rate paired with the minimal assessment time of the four identified features may act as an effective starting point when screening for cognitive impairment defined at CDR 0.5 and above.

Fluoxetine Protects against Dendritic Spine Loss in Middle-aged APPswe/PSEN1dE9 Double Transgenic Alzheimer’s Disease Mice

2020 ◽  
Vol 17 (1) ◽  
pp. 93-103 ◽  
Author(s):  
Jing Ma ◽  
Yuan Gao ◽  
Wei Tang ◽  
Wei Huang ◽  
Yong Tang
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Dendritic Spines ◽  
Dendritic Spine ◽  
Early Stage ◽  
Learning Ability ◽  
Middle Aged ◽  
Protein Levels ◽  
Spine Pathology ◽  
The Impact

Background: Studies have suggested that cognitive impairment in Alzheimer’s disease (AD) is associated with dendritic spine loss, especially in the hippocampus. Fluoxetine (FLX) has been shown to improve cognition in the early stage of AD and to be associated with diminishing synapse degeneration in the hippocampus. However, little is known about whether FLX affects the pathogenesis of AD in the middle-tolate stage and whether its effects are correlated with the amelioration of hippocampal dendritic dysfunction. Previously, it has been observed that FLX improves the spatial learning ability of middleaged APP/PS1 mice. Objective: In the present study, we further characterized the impact of FLX on dendritic spines in the hippocampus of middle-aged APP/PS1 mice. Results: It has been found that the numbers of dendritic spines in dentate gyrus (DG), CA1 and CA2/3 of hippocampus were significantly increased by FLX. Meanwhile, FLX effectively attenuated hyperphosphorylation of tau at Ser396 and elevated protein levels of postsynaptic density 95 (PSD-95) and synapsin-1 (SYN-1) in the hippocampus. Conclusion: These results indicated that the enhanced learning ability observed in FLX-treated middle-aged APP/PS1 mice might be associated with remarkable mitigation of hippocampal dendritic spine pathology by FLX and suggested that FLX might be explored as a new strategy for therapy of AD in the middle-to-late stage.

scholarly journals Association between methylation of BIN1 promoter in peripheral blood and preclinical Alzheimer’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hao Hu ◽  
Lan Tan ◽  
Yan-Lin Bi ◽  
Wei Xu ◽  
Lin Tan ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Peripheral Blood ◽  
Late Onset ◽  
Early Stage ◽  
Susceptibility Gene ◽  
Subjective Cognitive Decline ◽  
Preclinical Ad ◽  
T Tau ◽  
New Evidence

AbstractThe bridging integrator 1 (BIN1) gene is the second most important susceptibility gene for late-onset Alzheimer’s disease (LOAD) after apolipoprotein E (APOE) gene. To explore whether the BIN1 methylation in peripheral blood changed in the early stage of LOAD, we included 814 participants (484 cognitively normal participants [CN] and 330 participants with subjective cognitive decline [SCD]) from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database. Then we tested associations of methylation of BIN1 promoter in peripheral blood with the susceptibility for preclinical AD or early changes of cerebrospinal fluid (CSF) AD-related biomarkers. Results showed that SCD participants with significant AD biological characteristics had lower methylation levels of BIN1 promoter, even after correcting for covariates. Hypomethylation of BIN1 promoter were associated with decreased CSF Aβ42 (p = 0.0008), as well as increased p-tau/Aβ42 (p = 0.0001) and t-tau/Aβ42 (p < 0.0001) in total participants. Subgroup analysis showed that the above associations only remained in the SCD subgroup. In addition, hypomethylation of BIN1 promoter was also accompanied by increased CSF p-tau (p = 0.0028) and t-tau (p = 0.0130) in the SCD subgroup, which was independent of CSF Aβ42. Finally, above associations were still significant after correcting single nucleotide polymorphic sites (SNPs) and interaction of APOE ɛ4 status. Our study is the first to find a robust association between hypomethylation of BIN1 promoter in peripheral blood and preclinical AD. This provides new evidence for the involvement of BIN1 in AD, and may contribute to the discovery of new therapeutic targets for AD.

scholarly journals Plasma contact factors as novel biomarkers for diagnosing Alzheimer’s disease

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jung Eun Park ◽  
Do Sung Lim ◽  
Yeong Hee Cho ◽  
Kyu Yeong Choi ◽  
Jang Jae Lee ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Stage ◽  
Clinical Stage ◽  
Area Under The Curve ◽  
Contact System ◽  
Plasma Diagnostic ◽  
Vascular Abnormalities ◽  
Prodromal Ad ◽  
Novel Biomarkers

Abstract Background Alzheimer’s disease (AD) is the most common cause of dementia and most of AD patients suffer from vascular abnormalities and neuroinflammation. There is an urgent need to develop novel blood biomarkers capable of diagnosing Alzheimer’s disease (AD) at very early stage. This study was performed to find out new accurate plasma diagnostic biomarkers for AD by investigating a direct relationship between plasma contact system and AD. Methods A total 101 of human CSF and plasma samples from normal and AD patients were analyzed. The contact factor activities in plasma were measured with the corresponding specific peptide substrates. Results The activities of contact factors (FXIIa, FXIa, plasma kallikrein) and FXa clearly increased and statistically correlated as AD progresses. We present here, for the first time, the FXIIa cut-off scores to as: > 26.3 U/ml for prodromal AD [area under the curve (AUC) = 0.783, p < 0.001] and > 27.2 U/ml for AD dementia (AUC = 0.906, p < 0.001). We also describe the cut-off scores from the ratios of CSF Aβ1–42 versus the contact factors. Of these, the representative ratio cut-off scores of Aβ1–42/FXIIa were to be: < 33.8 for prodromal AD (AUC = 0.965, p < 0.001) and < 27.44 for AD dementia (AUC = 1.0, p < 0.001). Conclusion The activation of plasma contact system is closely associated with clinical stage of AD, and FXIIa activity as well as the cut-off scores of CSF Aβ1–42/FXIIa can be used as novel accurate diagnostic AD biomarkers.

scholarly journals Classifying Non-Dementia and Alzheimer’s Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: The Digital Clock Drawing Test

2021 ◽  
Vol 82 (1) ◽  
pp. 47-57 ◽  
Author(s):  
Anis Davoudi ◽  
Catherine Dion ◽  
Shawna Amini ◽  
Patrick J. Tighe ◽  
Catherine C. Price ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Vascular Dementia ◽  
Area Under The Curve ◽  
Clock Drawing Test ◽  
Digital Clock ◽  
Clock Drawing ◽  
Drawing Test ◽  
Dementia Patients ◽  
Optimal Area

Background: Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective: To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer’s disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer’s disease (AD) versus vascular dementia (VaD). Methods: Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer’s disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models. Results: When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features. Conclusion: The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.

scholarly journals Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

2021 ◽  
Vol 11 (4) ◽  
pp. 1574
Author(s):  
Shabana Urooj ◽  
Satya P. Singh ◽  
Areej Malibari ◽  
Fadwa Alrowais ◽  
Shaeen Kalathil
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Early Detection ◽  
Differential Evolution ◽  
Early Stage ◽  
Selection Process ◽  
Parameter Tuning ◽  
Wavelet Neural Network ◽  
Tuning Parameter

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC).

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